8 Zooarchaeology
8.1 Case studies
The following map shows the sites under investigation, divided by chronology. Please select the desired chronology (or chronologies) from the legend on the right.
Legend: R = Roman, LR = Late Roman, EMA = Early Middle Ages, Ma = 11th c. onwards
8.2 Medians
The faunal dataset is large (434+ records) and diversified. Looking at the distributions of each animal, the curve is not gaussian. The best choice for non-normal curves is to use medians instead of means to come up with figures that are less dependent on outliers. The function Medians_Chrono_Zoo() (Section 3.1) can be used to return as output weighted medians for each chronology. The in-depth description of how weights are calculated for each sample can be found in Section 6.4.1. To summarise, sites with a very large (i.e. fuzzy) chronology contribute less to the calculation of the median. Table 8.1 provides the median values of the main categories of faunal remains for each chronology, and Table 8.2 the median values for each century. Stronger colours in the cells indicate higher values.
Show the code
Medians_Categorised_per_Chronology_ZOO <-
data.frame(
Medians_Chrono_Zoo(zooarch_cond, "R")*100,
Medians_Chrono_Zoo(zooarch_cond, "LR")*100,
Medians_Chrono_Zoo(zooarch_cond, "EMA")*100,
Medians_Chrono_Zoo(zooarch_cond, "Ma")*100
)
# Round to 2 digits
Medians_Categorised_per_Chronology_ZOO <- round(Medians_Categorised_per_Chronology_ZOO, 2)
## Weighted medians per century ##
Medians_ZOO_Centuries <- data.frame(
"I BCE" = zooarch_tables(zooarch_cond, -1)$Medians,
"I CE" = zooarch_tables(zooarch_cond, 1)$Medians,
"II CE" = zooarch_tables(zooarch_cond, 2)$Medians,
"III CE" = zooarch_tables(zooarch_cond, 3)$Medians,
"IV CE" = zooarch_tables(zooarch_cond, 4)$Medians,
"V CE" = zooarch_tables(zooarch_cond, 5)$Medians,
"VI CE" = zooarch_tables(zooarch_cond, 6)$Medians,
"VII CE" = zooarch_tables(zooarch_cond, 7)$Medians,
"VIII CE" = zooarch_tables(zooarch_cond, 8)$Medians,
"IX CE" = zooarch_tables(zooarch_cond, 9)$Medians,
"X CE" = zooarch_tables(zooarch_cond, 10)$Medians,
"XI CE" = zooarch_tables(zooarch_cond, 11)$Medians
)
# Assigning the colnames (optional - instead of roman numerals)
colnames(Medians_ZOO_Centuries) <- c("1st c. BCE", "1st c. CE", "2nd c.", "3rd c.", "4th c.", "5th c.", "6th c.", "7th c.", "8th c.", "9th c.", "10th c.", "11th c.")
# Rounding the medians
Medians_ZOO_Centuries <- round(Medians_ZOO_Centuries, digits=2)
# Removing categories that are not necessary
Medians_ZOO_Centuries <- Medians_ZOO_Centuries[-c(6:9),]| R | LR | EMA | Ma | |
|---|---|---|---|---|
Pigs 
|
48.00 | 38.62 | 35.00 | 35.62 |
Cattle 
|
10.75 | 8.36 | 18.00 | 19.00 |
Caprine 
|
25.00 | 22.08 | 29.00 | 27.00 |
Dom..Fowl 
|
4.00 | 6.00 | 5.00 | 5.00 |
Edible.W..Mammals 
|
5.00 | 3.00 | 3.00 | 3.00 |
Fish 
|
1.00 | 2.00 | 3.93 | 1.00 |
Mollusca 
|
11.00 | 8.00 | 4.00 | 2.89 |
Unedible.Dom..Mammals 
|
2.00 | 3.00 | 3.95 | 2.00 |
Unedible.Wild.Mammals 
|
1.00 | 1.00 | 1.00 | 1.00 |
Pigs’ medians from the Italian peninsula are the highest in each chronology, although their values decrease after the Roman age peak. Cattle medians slightly decrease after the Roman age, even though surprisingly (put a reference here to literature review to explain why surprisingly) the values increase again (18–19.71%) during the early Medieval and Medieval age. The trends for sheeps and goats are also interesting. During the Roman age the Italian median is 25%, slightly decreasing in the 3rd to the 5th century, and increasing again after. When discussing sheep-farming, one must always consider the geographical features from which the data is being collected. This will be discussed later on in the chapter, where more regional and geographical trends will be provided. Domestic fowl (chickens and geese) has quite stable values of 4-5%, with a peak of 7.68% in the 11th century. Wild game peaks during the Roman age, with a median value of 5%, reaching a minimum in the early Middle ages (2%) and rising again in the 11th century. Two considerations must be made for game consumption. The first is that as we will see later on, game consumption is strongly related to the site typology. Secondly, the Roman age value is pulled up by assemblages from the 1st century BCE. After that, the values strongly decrease and by looking at the individual centuries the medians from the 7th century onwards are much higher (ranging from 1.42% to 2.09%).
| Pigs | Cattle | Caprine | Domestic fowl | Wild game | |
|---|---|---|---|---|---|
| 1st c. BCE | 39.70 | 11.41 | 26.07 | 0.00 | 0.76 |
| 1st c. CE | 40.89 | 11.31 | 25.48 | 0.00 | 0.58 |
| 2nd c. | 48.08 | 10.28 | 22.58 | 0.79 | 0.00 |
| 3rd c. | 41.57 | 8.27 | 19.42 | 1.57 | 0.72 |
| 4th c. | 34.14 | 9.58 | 23.36 | 1.85 | 0.97 |
| 5th c. | 34.05 | 13.33 | 24.59 | 2.33 | 0.90 |
| 6th c. | 31.83 | 20.66 | 28.55 | 2.75 | 0.65 |
| 7th c. | 30.42 | 18.52 | 30.14 | 4.51 | 1.58 |
| 8th c. | 33.63 | 13.89 | 30.13 | 2.70 | 1.18 |
| 9th c. | 37.54 | 11.76 | 23.32 | 1.16 | 1.54 |
| 10th c. | 35.77 | 14.36 | 25.64 | 1.63 | 1.93 |
| 11th c. | 34.08 | 19.34 | 27.44 | 1.91 | 2.09 |
| * The color gradients in this table are used to indicate the chronologies. |
9 Precision plots
The following plots for the posterior distributions of the beta-binomial models will also come with a precision parameter \(\phi\), that shapes the beta curve. What is the correct way to interpret it? A \(\phi\) value smaller than 2 indicates that there is extreme overdispersion in the dataset, and that probabilities around 0 and 1 are more likely than the mean. On the contrary, a \(\phi\) value greater than 2 indicates more precision in the dataset. Although this is often the case of larger datasets, where most of the values shrink towards the average, it also indicates a coherent dataset (where values are similar). Finally, if \(\phi\) equals 2 every probability between 0 and 1 is likely possible.
10 Chronology
10.1 Trends by century
To estimate the animals’ occurrences probability in each century, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an index \({[Century]}\) as the model will provide estimates for each century under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by century.
\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]
\[ logit(\bar{p}_{i}) = \alpha_{[Century]} \]
\[ \alpha_{[Century]} \sim Normal(0,1.5) \]
\[ \phi_{[Century]} \sim Exponential(1) \]
10.2 Trends by traditional chronologies
To estimate the animals’ occurrences probability in each chronology, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an index \({[ChrID]}\) as the model will provide estimates for each chronology under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by chronology.
\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]
\[ logit(\bar{p}_{i}) = \alpha_{[ChrID]} \]
\[ \alpha_{[ChrID]} \sim Normal(0,1.5) \]
\[ \phi_{[ChrID]} \sim Exponential(1) \]
10.2.1 Community plot
11 Context type
11.1 Pigs
To estimate the animals’ occurrences probability in each chronology and context, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an interaction index \({[TCid]}\) as the model will provide estimates for each context type and chronology under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by chronology and context type.
\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]
\[ logit(\bar{p}_{i}) = \alpha_{[TCid]} \]
\[ \alpha_{[TCid]} \sim Normal(0,1.5) \]
\[ \phi_{[TCid]} \sim Exponential(1) \]
11.2 Cattle
11.3 Caprine
Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.
11.4 Edible W. Mammals
11.5 Community plot
12 Macroregion
To estimate the animals’ occurrences probability in each chronology and macroregion, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an interaction index \({[REGid]}\) as the model will provide estimates for each macroregion and chronology under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by chronology and macroregion.
\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]
\[ logit(\bar{p}_{i}) = \alpha_{[REGid]} \]
\[ \alpha_{[REGid]} \sim Normal(0,1.5) \]
\[ \phi_{[REGid]} \sim Exponential(1) \]
12.1 Pigs
12.2 Cattle
12.3 Caprine
12.4 Edible W. Mammals
13 Geography
To estimate the animals’ occurrences probability in each chronology and geography, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an interaction index \({[GEOid]}\) as the model will provide estimates for each geography and chronology under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by chronology and geography.
\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]
\[ logit(\bar{p}_{i}) = \alpha_{[GEOid]} \]
\[ \alpha_{[GEOid]} \sim Normal(0,1.5) \]
\[ \phi_{[GEOid]} \sim Exponential(1) \]
13.1 Pigs
13.2 Cattle
13.3 Caprine
13.4 Edible W. Mammals
14 Altitude
The proposed model to estimate the probability of occurrence as related to the altitude (the slope \(\beta\)) and chronology (\({[ChrID]}\)) uses a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is a simple intercept with slope model, where the intercept \(\alpha\) carries an index \({[ChrID]}\) as the model provides estimates for each chronology under examination. A single \(\phi\) parameter indicates the precision in the Beta distribution.
\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]
\[ logit(\bar{p}_{i}) = \alpha_{[ChrID]} + \beta_{[ChrID]}\cdot Alt_{i} \]
\[ \alpha_{ChrID} \sim Normal(0,1.5) \]
\[ \beta_{ChrID} \sim Normal(0,1.5) \]
\[ \phi \sim Exponential(1) \]
OGR data source with driver: ESRI Shapefile
Source: "/Users/robertoragno/Desktop/University/Bari/PhD - Quarto/Italy_SHP/ne_110m_coastline/ne_110m_coastline.shp", layer: "ne_110m_coastline"
with 134 features
It has 3 fields
Integer64 fields read as strings: scalerank 14.1 Pigs
14.2 Cattle
14.3 Caprine
14.4 Edible W. Mammals
14.5 Community plot
